Reposted by fairseq2
In line with FAIR mission, we open source Seamless Interaction Dataset to the community in order to accelerate the field. Download on github github.com/facebookrese... and @hf.co huggingface.co/datasets/fac...
GitHub - facebookresearch/seamless_interaction: Foundation Models and Data for Human-Human and Human-AI interactions.
Foundation Models and Data for Human-Human and Human-AI interactions. - facebookresearch/seamless_interaction
github.com
June 27, 2025 at 6:16 PM
In line with FAIR mission, we open source Seamless Interaction Dataset to the community in order to accelerate the field. Download on github github.com/facebookrese... and @hf.co huggingface.co/datasets/fac...
[4/4] Collaborate efficiently with reproducible experiment setups using FAIRSeq2. Identify root causes swiftly and share lessons learned with the community. Create your benchmarks and contribute!
June 17, 2025 at 11:01 AM
[4/4] Collaborate efficiently with reproducible experiment setups using FAIRSeq2. Identify root causes swiftly and share lessons learned with the community. Create your benchmarks and contribute!
[3/4] Beyond TensorBoard and WanDB, FAIRSeq2 supports torch profilers (set trainer.profile and common.profilers.torch.enabled=True) to inspect potential infra issues. Dive deep into your training processes with various profilers and metric recorders.
June 17, 2025 at 11:01 AM
[3/4] Beyond TensorBoard and WanDB, FAIRSeq2 supports torch profilers (set trainer.profile and common.profilers.torch.enabled=True) to inspect potential infra issues. Dive deep into your training processes with various profilers and metric recorders.
[2/4] e.g., the tokens/s metric allows easy computation of Model FLOP Utilization – a great metric to check resource utilization efficiency. We achieve up to 48% MFU on 8 GPUs and maintain 37.6% across 4 nodes (32 GPUs). Experience effective and efficient distributed training!
June 17, 2025 at 11:01 AM
[2/4] e.g., the tokens/s metric allows easy computation of Model FLOP Utilization – a great metric to check resource utilization efficiency. We achieve up to 48% MFU on 8 GPUs and maintain 37.6% across 4 nodes (32 GPUs). Experience effective and efficient distributed training!
Nothing explains better than a vivid example:
February 19, 2025 at 2:02 PM
Nothing explains better than a vivid example:
Large Concept Models extends fairseq2 and demonstrates what we can imagine beyond traditional sequence models -- check it out here: ai.meta.com/research/pub...
Large Concept Models: Language Modeling in a Sentence Representation Space | Research - AI at Meta
LLMs have revolutionized the field of artificial intelligence and have emerged as the de-facto tool for
many tasks. The current established technology of...
ai.meta.com
February 14, 2025 at 10:18 AM
Large Concept Models extends fairseq2 and demonstrates what we can imagine beyond traditional sequence models -- check it out here: ai.meta.com/research/pub...
Seamless Communication as the best innovation recognized by TIMES and Nature -- check it out here: ai.meta.com/research/sea...
Seamless Communication - AI at Meta
A significant step towards removing language barriers through expressive, fast and high-quality AI translation
ai.meta.com
February 14, 2025 at 10:18 AM
Seamless Communication as the best innovation recognized by TIMES and Nature -- check it out here: ai.meta.com/research/sea...
Check out our doc for more details: facebookresearch.github.io/fairseq2/stabl…
https://facebookresearch.github.io/fairseq2/stabl…
February 12, 2025 at 12:32 PM
Check out our doc for more details: facebookresearch.github.io/fairseq2/stabl…